Overview

Our LUR sampling scheme involved collocated sampling at three EPA monitoring sites in the Denver metropolitan area. Two of these sites (Globeville and National Jewish) only have PM2.5 monitoring data. However, the near-road monitoring site at I-25 has both PM2.5 and black carbon.

I’ve been using the monitoring data from the I-25 site to fit a calibration curve (regression model) for the UPAS PM2.5 and BC data. The PM2.5 calibration model was decent, whereas the calibration curve for BC (using only the UPAS measurement to predict the monitor measurement) was terrible. I also fit calibration models for PM2.5 using data from the Globeville and National Jewish sites. These models performed similarly to the I-25 site model

The regression model fit for the BC data across all campaigns was pretty poor. There didn’t appear to be any correlation between the monitor data and the UPAS data. However, once the data were stratified by campaign, there were some obvious relationships. For some reason, the UPAS monitors performed differently based on the campaign. Notably, there was a negative relationship for the winter data. Christian L’Orange (Powerhouse) proposed a couple possibilities. First, ambient temperature might be an issue (e.g., there may be discrepancies between the actual sampled volume and the recorded sampling volume due to differences in density at cold temperature or the UPAS might have been prone to leaks). Second, differences in aerosol composition could also be an issue– there is a delay between sample collection and the SootScan, and more volatile oragnics that absorb light at 880 nm could have been measured by the aethalometer and not by the SootScan. Importantly, the PM2.5 data doesn’t seem to have this seasonal variability in UPAS vs. EPA monitor data, so temperature is less likely. Shantanu Jathar (CSU Mechanical Engineering) favored the “differences in aerosol composition” hypothesis, though it’s difficult to examine with the data we have.

One option address this fit issue was to fit separate calibration curves for each campaign. However, we did not have collocated data for Campaign 1 (late spring/summer) because we weren’t able to arrange access to the I-25 site before the summer. Separate seasonal models will require us to figure out which curve works best for the Campaign 1 data.

I attempted to fit multivariate models with indicators for campaign and temperature, and was able to get a regression that explained >60% of the variability in filter-based BC concentrations, but using this model to adjust the filter BC concentrations didn’t work. BUT! It provided evidence that season-specific models were most appropriate.

Sheryl and I met with some resarchers at CSU (Shantanu Jathar, Ellison Carter, John Volckens, Ander Wilson, and Christian L’Orange) to talk about the issues we were having with the UPAS data. Some suggestions from this meeting included:

  • Using Deming (orthogonal) regression to fit a curve for the BC data due to errors in both the SootScan and the aethalometer. JV recommended we assume equal variances in the errors.
  • Looking at trends in the metals data (i.e., S, K, and Fe) from the nearby CSN site (not a co-location site) to see if there are similar trends (i.e., lower levels in the winter) that might corroborate our BC time trends

The rest of this document walks through what we ended up doing to adjust the UPAS data. Ultimately I used both linear regression and deming regression to calibrate the filter-based PM2.5 and BC concentrations.

One outstanding issue will be to identify why there were differences by campaign. This might require some further investigation. We might want to have the UPAS checked out (e.g., check for leaks, check to see if the flow is still calibrated correctly, etc.). Some possibilities could be differences in aerosol composition that affected BC measurements (e.g., EPA measurements are near direct read, whereas our filter-based methods are integrated and there is a long delay between sample collection and measurement); interference from metals; and issues with the UPAS in cold weather. I plotted some of the UPAS parameters and didn’t see any obvious differences, aside some some issues at the end of one of the sampling runs for the first week of Campaign 4 (mass flow and volumetric flow rate drop off that the end of the run).

Update: We currently have UPAS co-locating with the CSN site in Denver. Perhaps this dataset will shed some light on the seasonal issues we are seeing in our data.

PM2.5 Calibration

First, we identified all of the UPAS filters co-located with the I-25 monitor. Over the course of the four sampling campaigns, we had 56 filters co-located with the three PM2.5 monitors (17 at the I-25 site). The raw PM2.5 time-weighted average concentrations from the UPAS filters at the I-25 site averaged 8.5 \(\mu\)g/m\(^3\) and had a range of 2.3 to 18.0 \(\mu\)g/m\(^3\).

Summary of the raw PM2.5 measurements at the I-25 UPAS monitors:

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.290   4.926   7.495   8.489  11.445  17.989

Temporal trends for the UPAS filter results were as expected. Concentrations were highest in the winter (Campaign 4) and summer (Campaign 2) due to increases in biomass burning and lower in the spring and fall (Campaigns 1 and 3, respectively). On average, concentrations were higher at the I-25 and Globeville sites compared to the National Jewish site. This result was expected, as the I-25 and Globeville sites are near major highways.

Figure 1. UPAS PM~2.5~ Time series

Figure 1. UPAS PM2.5 Time series

The EPA monitoring data showed similar patterns to the UPAS filter data time series. There was a spike in PM2.5 in September of 2018, likely due to nearby wildfires.

Figure 2. UPAS PM~2.5~ Time series

Figure 2. UPAS PM2.5 Time series

We used each monitor in a separate simple linear regression model to identify the best fit to calibrate the full UPAS filter data set. Overall, there was modest agreement between the UPAS and EPA monitor results. The best overall fit was for the I-25 monitor. Diagnostic plots showed that the linear model fit reasonably well and was appropriate.

Figure 3. Scatter plots for PM2.5 (UPAS vs EPA)

Figure 3. Scatter plots for PM2.5 (UPAS vs EPA)

The diagnostic plots for the linear regression model using data for the I-25 siteshowed that the model fit reasonably well.

## 
## Call:
## lm(formula = pm_ug_m3 ~ monitor_mean, data = filter(cal_data, 
##     monitor_id == "080310027"))
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -5.396 -2.766 -1.106  2.945  7.003 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.6137     2.4309   0.252  0.80412   
## monitor_mean   0.7071     0.2041   3.464  0.00347 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.548 on 15 degrees of freedom
## Multiple R-squared:  0.4444, Adjusted R-squared:  0.4074 
## F-statistic:    12 on 1 and 15 DF,  p-value: 0.003471

We used the linear regression model based on the I-25 monitoring data to calibrate the remaining filters. We then used box plots to compare raw and calibrated UPAS PM2.5 results to all EPA monitors in Adams, Arapaho, and Denver counties. These plots are shown below. Overall, there appears to be reasonable agreement between the calibrated UPAS data and general trends across the region.

Figure 4. Box plots of PM~2.5~ by month

Figure 4. Box plots of PM2.5 by month

Figure 5. Box plots of PM~2.5~ by season

Figure 5. Box plots of PM2.5 by season

BC Calibration

The BC calibration process was originally less successful than the PM2.5 calibration. Again, we are using aethalometer data from the I-25 site because that is the only location in the Denver area with available data. UPAS BC results were similar whether blank-corrected or not, so we elected to use the blank-corrected BC concentrations. (Note: Only Campaign 1 needed to be blank corrected– see 00_Analysis_Notes). Raw UPAS BC concentrations averaged 3.2 \(\mu\)g/m3 and ranged from 0.3 to 5.8 \(\mu\)g/m3.

Time Series- UPAS

Unlike the PM2.5 data at this site, temporal trends for the UPAS filter concentrations were unexpected. Our UPAS data showed declining BC concentrations at the I-25 site, with the lowest concentrations occurring in the winter. The time-series plot suggested that there may be differences in how BC was measured by campaign.

Figure 6. UPAS BC Time series

Figure 6. UPAS BC Time series

Time Series- EPA Monitor

The EPA monitoring data were also highly variable, but the I-25 site showed lower concentrations in the spring and level concentrations across the summer, fall, and winter seasons, likely attributable to consistent traffic and the influence of biomass burning in the summer and winter.

Figure 7. UPAS BC Time series

Figure 7. UPAS BC Time series

Comparing EPA Data to CDPHE Data

To be thorough, I wanted to compare the CDPHE BC data for the campaign periods to the EPA campaign periods to see if there was a difference how CDPHE processed their data. Comparisons between the raw CDPHE data received from Brad Rink and the BC data from the EPA website suggested near perfect agreement, so there was no issue using the EPA data.

I’ve reached out to Brad re: the maintenance and operation of the AE33 at the I-25 site. The (AE33 user manual)[ftp://aftp.cmdl.noaa.gov/aerosol/doc/inst/aeth/ae33_sop_20161227.pdf] states that the sigma value is 7.77 m2/g, which is multiplied by a multiple scattering parameter C = 1.57. The value I have for sigma in the spreadsheets from Christian is 4.2 \(\mu\)g/cm2, with a standard area of 7.065 cm2.

Scatter plot of CDPHE vs. EPA suggests near perfect agreement.
Figure 8. Scatterplot of CDPHE vs. EPA data

Figure 8. Scatterplot of CDPHE vs. EPA data

Time series plots for CDPHE and EPA also suggest that agreement between the two data sets is high.
Figure 8. Time series of CDPHE and EPA BC data

Figure 8. Time series of CDPHE and EPA BC data

Metals

Sulfur/Potassium Ratios

Christian suggested that there might be differences in the metals and/or differences in aerosols sampled across seasons that could interfere with the transmissometry data. A quick summary of the S and K concentrations and the S/K ratios measured for the filters suggests that the black carbon at this site is primarily from traffic (all ratios are above 1, but the ratio varies with time). S is associated with fossil fuel combustion, whereas K is more strongly linked to biomass burning. The S/K ratio averages 3.4, but ranges from 1.4 to 8.6 (IQR = 2.5). A time series plot suggests that S/K ratios for this near-road monitor are highest in the winter.

Figure 9. S/K ratios for I-25 site filters

Figure 9. S/K ratios for I-25 site filters

Figure 9. S/K ratios for I-25 site filters

Figure 9. S/K ratios for I-25 site filters

Figure 9. S/K ratios for I-25 site filters

Figure 9. S/K ratios for I-25 site filters

The Yuma St. monitor doesn’t report metals data, but the Navajo St. site (3.3 miles away from the Yuma St. site) does. The S and K concentrations and S/K ratios are plotted below. S/K ratio at this site averages 2.6 and ranges from 0 to 26.8 (IQR = 3.3). These ratios also show a bit of an uptick during the winter of 2019, but this is not a near-road site.

Figure 10. S/K ratios for the nearby Navajo St. monitoring site

Figure 10. S/K ratios for the nearby Navajo St. monitoring site

Figure 10. S/K ratios for the nearby Navajo St. monitoring site

Figure 10. S/K ratios for the nearby Navajo St. monitoring site

Figure 10. S/K ratios for the nearby Navajo St. monitoring site

Figure 10. S/K ratios for the nearby Navajo St. monitoring site

Iron

John Volkens cited a study looking at how iron might interfere with absorption (White et al. 2016). The filter data show a bit of an uptick in Campaign 4, though it could just be more variability. A time series of iron concentrations at the Navajo St site (again– the closest monitoring site with metals data to the I-25 site) shows Fe concentrations are similarly messy in winter of 2019.

Figure 11. Iron concentrations for I-25 site collocated monitors

Figure 11. Iron concentrations for I-25 site collocated monitors

Figure 12. Iron concentrations for the nearby Navajo St. monitoring site

Figure 12. Iron concentrations for the nearby Navajo St. monitoring site

Elemental Carbon and OC/EC ratios

Shantanu had the thought of comparing the BC data measured at the I-25 site with the EC data measured at the Navajo St. Site and comparing the two measurements. Note again that the Navajo St. site is about 3 miles from the I-25 site and is not considered a “near road” location. The time series plot shows EC concentrations are higher in the winter and appear to have higher variability in the winter of 2019.

Figure 13. EC concentrations for the nearby Navajo St. monitoring site

Figure 13. EC concentrations for the nearby Navajo St. monitoring site

Figure 13. EC concentrations for the nearby Navajo St. monitoring site

Figure 13. EC concentrations for the nearby Navajo St. monitoring site

Figure 13. EC concentrations for the nearby Navajo St. monitoring site

Figure 13. EC concentrations for the nearby Navajo St. monitoring site

Comparing UPAS BC to EPA BC

When we compared the UPAS TWA BC concentrations to the EPA monitoring data, we saw pretty poor fit (Figure 14). Once stratified by campaign, however, it became evident that there were differences in each campaign (Figure 15).

Figure 14. Scatter plots for BC (UPAS vs EPA)

Figure 14. Scatter plots for BC (UPAS vs EPA)

Figure 15. Scatter plots for BC (UPAS vs EPA) stratified by campaign

Figure 15. Scatter plots for BC (UPAS vs EPA) stratified by campaign

I-25 UPAS vs. Nearest UPAS (Speer Blvd. Park)

To see if there was anything different about the Yuma st. site, I compared the calibration filters to filters from the closest site (Speer Blvd. Park) and other nearby sites that aren’t near a major road (City Park and Mestizo-Curtis Park).

Figure 16. Time series of filters from I-25 site and nearby site data

Figure 16. Time series of filters from I-25 site and nearby site data

Initial Regression Models

Nothing in the above analyses helped explain why our BC data were so weird, so we had to proceed with calibration.

Our regression model to fit the EPA monitoring data with the UPAS data was very poor. The correlation between UPAS BC and monitor BC was 0.18. The linear regression resulted in a negative adjusted R2 value (-0.01), though diagnostic plots showed that the linear model fit was appropriate.

## 
## Call:
## lm(formula = bc_ug_m3 ~ monitor_mean, data = cal_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7323 -1.7469  0.6027  1.4386  2.8136 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)   2.79701    2.26172   1.237    0.237
## monitor_mean  0.09281    1.21907   0.076    0.940
## 
## Residual standard error: 1.971 on 14 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.0004139,  Adjusted R-squared:  -0.07099 
## F-statistic: 0.005797 on 1 and 14 DF,  p-value: 0.9404

Deming Regression

We presented the OLS models to our colleagues from Engineering to get their thoughts. John Volckens thought that Deming regression might be more appropriate, so that’s what we did. Deming (orthoganol) regression accounts for errors in both instruments (aethalometer and UPAS/transmissometer). We assumed equal variance in the errors based on JV’s recommendation. I’m still looking for an empirical study to site. The OLS and Deming curves are shown below. We stratified the regression by campaign because we saw some differences depending on when the filters were sampled

Figure 17. Box plots of raw and calibrated BC by month

Figure 17. Box plots of raw and calibrated BC by month

Calibration of spring data

Because we could not co-locate a UPAS with the aethalometer during the late spring/summer season, we had to determine which season’s curve would best fit the Campaign 1 data. We decided to use the Campaign 2 (summer) data because the distribution of raw measurements and temperatures for Campaign 1 were most similar to Campaign 2.

Comparison of raw and calibrated BC Data

We used separate Deming regression models (raw ~ int + beta*aeth) to calibrate each campaign’s raw BC data. Then the calibrated meausrements were combined into a single data set.

After using the models to calibrate the full filter data set, we used box plots to compare raw and calibrated UPAS BC results to the EPA monitor at the I-25 site. These plots are shown below.

Figure 18. Box plots of raw and calibrated BC by month

Figure 18. Box plots of raw and calibrated BC by month

Figure 18. Box plots of raw and calibrated BC by month

Figure 18. Box plots of raw and calibrated BC by month

Figure 19. Box plots of raw and calibrated BC by season

Figure 19. Box plots of raw and calibrated BC by season

Figure 19. Box plots of raw and calibrated BC by season

Figure 19. Box plots of raw and calibrated BC by season

Plotting some UPAS parameters for the monitors collocated with the EPA aethalometer

We wanted to see if there were any issues with the UPAS operating parameters.

UPAS Metadata Summary Table

filter_id Location UPASserial MonitorNumber Address Details_GPS Campaign CampaignWeek FilterNumber UPASfirmware LifetimeSampleCount LifetimeSampleRuntime SAMPLE IDENTIFICATION UPASlogFilename SampleName CartridgeID SETUP SUMMARY GPSUTCOffset StartOnNextPowerUp ProgrammedStartDelay ProgrammedRuntime VolumetricFlowRate FlowOffset DutyCycle DutyCycleWindow GPSEnabled LogFileMode LogInterval AppLock AppVersion SAMPLE SUMMARY StartDateTimeUTC StartDateTimeLocal StartBatteryCharge StartBatteryVoltage EndDateTimeUTC EndDateTimeLocal EndBatteryCharge EndBatteryVoltage ShutdownMode SampledVolume SampledRuntime LoggedRuntime AverageVolumetricFlowRate week campaign
200202 9th and Yuma PS0420 20 9th and Yuma Outdoor – Location #28 2 03 02 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 12 674.37 NA /sd/PS0420_LOG_2018-07-25T14_20_37UTC_—————____16p.txt ————— 16p NA -6 0 0 360000000 1 0 80 30 0 0 30 0 i2.1.5 NA 2018-07-25 14:20:37 2018-07-25 08:20:37 099 4.16 2018-07-31 04:57:00 2018-07-30 22:57:00 020 3.37 1 6450.69 107.681 134.601 0.998 Week3 Campaign2
200302 9th and Yuma PS0420 20 9th and Yuma Outdoor – Location #28 2 04 03 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 16 808.97 NA /sd/PS0420_LOG_2018-08-01T19_41_21UTC_200302_____________16p.txt 200302_________ 16p NA -6 0 0 360000000 1 0 80 30 0 0 30 0 i2.1.6 NA 2018-08-01 19:41:21 2018-08-01 13:41:21 099 4.16 2018-08-07 00:59:30 2018-08-06 18:59:30 029 3.48 1 6005.21 100.239 125.298 0.998 Week4 Campaign2
200402 9th and Yuma PS0420 20 9th and Yuma Outdoor – Location #28 2 05 04 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 17 934.27 NA /sd/PS0420_LOG_2018-08-08T23_40_32UTC_200402_____________16P.txt 200402_________ 16P NA -6 0 0 360000000 1 0 80 30 0 0 30 0 i2.1.6 NA 2018-08-08 23:40:32 2018-08-08 17:40:32 099 4.11 2018-08-13 15:53:00 2018-08-13 09:53:00 052 3.62 1 5376.27 89.764 112.204 0.998 Week5 Campaign2
200502 9th and Yuma PS0420 20 9th and Yuma Outdoor – Location #28 2 06 05 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 18 1046.47 NA /sd/PS0420_LOG_2018-08-16T07_23_20UTC_200502_____________16P.txt 200502_________ 16P NA -6 0 0 360000000 1 0 80 30 0 0 30 0 A2.1.5 NA 2018-08-16 07:23:20 2018-08-16 01:23:20 099 4.13 2018-08-21 01:16:30 2018-08-20 19:16:30 061 3.72 1 5460.89 91.107 113.883 0.999 Week6 Campaign2
200602 9th and Yuma PS0420 20 9th and Yuma Residence of participant #224 2 07 06 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 20 1160.35 NA /sd/PS0420_LOG_2018-08-23T03_39_21UTC_200602_____________16P.txt 200602_________ 16P NA -6 0 0 360000000 1 0 80 30 0 0 30 0 i2.1.6 NA 2018-08-23 03:39:21 2018-08-22 21:39:21 099 4.16 2018-08-28 01:10:00 2018-08-27 19:10:00 050 3.63 1 5634.64 94.007 117.509 0.999 Week7 Campaign2
200103 9th and Yuma PS0420 20 9th and Yuma Location #28 3 01 01 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 21 1277.86 NA /sd/PS0420_LOG_2018-10-09T21_55_08UTC_200103_____________16P.txt 200103_________ 16P NA -6 0 0 360000000 1 0 80 30 0 0 30 0 i2.1.6 NA 2018-10-09 21:55:08 2018-10-09 15:55:08 099 4.16 2018-10-14 16:31:30 2018-10-14 10:31:30 042 3.55 1 5491.68 91.683 114.603 0.998 Week1 Campaign3
200203 9th and Yuma PS0420 20 9th and Yuma Location #28 3 02 02 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 22 1392.46 NA /sd/PS0420_LOG_2018-10-16T18_08_12UTC_200203_____________16p.txt 200203_________ 16p NA -6 0 0 360000000 1 0 80 30 0 0 30 0 i2.1.6 NA 2018-10-16 18:08:12 2018-10-16 12:08:12 099 4.16 2018-10-21 17:58:30 2018-10-21 11:58:30 039 3.54 1 5741.80 95.869 119.836 0.998 Week2 Campaign3
200303 9th and Yuma PS0420 20 9th and Yuma Location #28 3 03 03 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 23 1512.30 NA /sd/PS0420_LOG_2018-10-23T22_12_57UTC_200303_____________16P.txt 200303_________ 16P NA -6 0 0 360000000 1 0 80 30 0 0 30 0 A2.1.5 NA 2018-10-23 22:12:57 2018-10-23 16:12:57 100 4.16 2018-10-28 20:29:00 2018-10-28 14:29:00 053 3.64 1 5666.18 94.613 118.264 0.998 Week3 Campaign3
200403 9th and Yuma PS0420 20 9th and Yuma Location #28 3 04 04 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 24 1630.56 NA /sd/PS0420_LOG_2018-10-30T20_29_07UTC_200403_____________16P.txt 200403_________ 16P NA -6 0 0 360000000 1 0 80 30 0 0 30 0 A2.1.5 NA 2018-10-30 20:29:07 2018-10-30 14:29:07 099 4.16 2018-11-04 16:59:00 2018-11-04 10:59:00 051 3.61 1 5582.84 93.197 116.495 0.998 Week4 Campaign3
200503 9th and Yuma PS0420 20 9th and Yuma Location #28 3 06 06 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 26 1865.86 NA /sd/PS0420_LOG_2018-11-14T14_14_02UTC_200503_____________16P.txt 200503_________ 16P NA -7 0 0 360000000 1 0 80 30 0 0 30 0 A2.1.5 NA 2018-11-14 14:14:02 2018-11-14 07:14:02 099 4.15 2018-11-19 21:00:30 2018-11-19 14:00:30 031 3.48 1 6071.38 101.419 126.772 0.998 Week6 Campaign3
200603 9th and Yuma PS0420 20 9th and Yuma Location #28 3 05 05 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 25 1747.06 NA /sd/PS0420_LOG_2018-11-06T20_35_57UTC_200603_____________16P.txt 200603_________ 16P NA -7 0 0 360000000 1 0 80 30 0 0 30 0 A2.1.5 NA 2018-11-06 20:35:57 2018-11-06 13:35:57 099 4.13 2018-11-11 19:24:00 2018-11-11 12:24:00 049 3.58 1 5689.82 95.040 118.798 0.998 Week5 Campaign3
490104 9th and Yuma PS0486 49 9th and Yuma Location #28 4 03 01 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 17 1463.81 NA /sd/PS0486_LOG_2019-02-06T08_37_43UTC_490204_____________16N.txt 490204_________ 16N NA -7 0 0 360000000 1 0 80 30 0 0 30 0 A2.1.6 NA 2019-02-06 08:37:43 2019-02-06 01:37:43 099 4.16 2019-02-08 10:59:30 2019-02-08 03:59:30 019 2.66 7 2385.00 40.289 50.361 0.987 Week 3 Campaign4
500104 9th and Yuma PS0420 50 9th and Yuma Location #28 4 01 01 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 27 1992.63 NA /sd/PS0420_LOG_2019-01-22T17_25_52UTC_500104_____________16L.txt 500104_________ 16L NA -7 0 0 360000000 1 0 80 30 0 0 30 0 A2.1.6 NA 2019-01-22 17:25:52 2019-01-22 10:25:52 099 4.16 2019-01-28 15:49:30 2019-01-28 08:49:30 013 2.80 2 6817.33 113.914 142.391 0.997 Week 1 Campaign4
490304 9th and Yuma PS0486 49 9th and Yuma Location #28 4 04 03 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 18 1514.17 NA /sd/PS0486_LOG_2019-02-12T23_25_31UTC_490304_____________16N.txt 490304_________ 16N NA -7 0 0 360000000 1 0 80 30 0 0 30 0 A2.1.6 NA 2019-02-12 23:25:31 2019-02-12 16:25:31 099 4.16 2019-02-17 18:29:30 2019-02-17 11:29:30 044 3.56 1 5512.06 92.052 115.064 0.998 Week 4 Campaign4
490404 9th and Yuma PS0486 49 9th and Yuma Location #28 4 05 04 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 19 1629.24 NA /sd/PS0486_LOG_2019-02-20T01_17_33UTC_490404_____________16N.txt 490404_________ 16N NA -7 0 0 360000000 1 0 80 30 0 0 30 0 i2.1.7 NA 2019-02-20 01:17:33 2019-02-19 18:17:33 099 4.16 2019-02-25 06:23:00 2019-02-24 23:23:00 016 2.79 2 5987.94 100.074 125.091 0.997 Week 5 Campaign4
490504 9th and Yuma PS0486 49 9th and Yuma Location #28 4 06 05 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 20 1754.33 NA /sd/PS0486_LOG_2019-02-26T22_36_54UTC_490504_____________16N.txt 490504_________ 16N NA -7 0 0 360000000 1 0 80 30 0 0 30 0 A2.1.9 NA 2019-02-26 22:36:54 2019-02-26 15:36:54 099 4.11 2019-03-04 14:19:30 2019-03-04 07:19:30 024 2.79 2 6497.28 108.569 135.710 0.997 Week 6 Campaign4
490604 9th and Yuma PS0486 49 9th and Yuma Location #28 4 07 06 UPAS_v2_0-rev0102-L152RE_20180510_mbed116.bin compiledMay 10 2018_17:58:45UTC 21 1890.04 NA /sd/PS0486_LOG_2019-03-08T01_21_57UTC_490604_____________16n.txt 490604_________ 16n NA -7 0 0 360000000 1 0 80 30 0 0 30 0 A2.1.9 NA 2019-03-08 01:21:57 2019-03-07 18:21:57 099 4.16 2019-03-12 17:01:00 2019-03-12 10:01:00 058 3.67 1 5348.17 89.321 111.651 0.998 Week 7 Campaign4